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AI agents act faster than humans can review. Here is how Vaikora enforces policy before that happens.

Product Updates · Oct 21, 2026 · 4 min read

When an AI agent calls a tool, it does not pause to ask whether that action is allowed. It executes. The policy question arrives after the fact, if anyone checks at all. That gap between action and review is where data leaves, rules break, and audits fail.

Vaikora closes that gap by sitting inline between the agent and whatever it calls: an LLM, a tool, an API, a file system. Every action passes through a policy decision point before execution. Allow, block, modify, or route to human approval. The action does not proceed until Vaikora has made that call.

Why "detect after execution" is the wrong model for AI agents

Detection after execution works for many threat categories. It does not work well for AI agent actions because the damage often happens in the action itself. An agent that exfiltrates a file, writes to a regulated database, or calls an external API with sensitive payload has already caused the problem by the time a detection fires. Rolling back agentic side effects is rarely clean, and sometimes impossible.

The right control point is before execution. That is a different architectural position than monitoring or logging. It requires being inline, not observing from the side.

How Vaikora sits inline

Vaikora deploys as a proxy between the agent runtime and the target. It supports the Model Context Protocol (MCP), which means it intercepts tool call traffic without requiring any SDK change in the agent. It also ships adapters for twelve hosted LLM providers.

When a call arrives, Vaikora runs it through six content inspection modules. Those modules check the payload against the active policy set, which can include built-in compliance presets for HIPAA, PCI-DSS, GDPR, SOC 2, and ISO 27001, or custom rules written by your team. The decision returned is deterministic: identical input always produces identical output. There is no probabilistic scoring that varies run to run.

Enforcement latency at P50 is 8ms, and at P99 is 45ms. Throughput reaches over 10,000 actions per second. For most agentic workflows, the policy gate adds no perceptible delay.

A concrete scenario: an AI coding agent attempts to call a tool that reads from a database containing fields tagged as PII. The agent passes the query through the Vaikora proxy. Vaikora evaluates the payload against the active data policy, identifies a regulated data pattern, and blocks the call. The agent receives a rejection response. No data is read. The decision is logged before the attempted execution completes.

What happens when a threat is serious enough to escalate

Not every policy violation is a block-and-move-on event. Vaikora maps high-severity decisions to external enforcement systems so that your SOC sees the signal, not just the audit log.

• Threats pushed to SentinelOne as Threat Intelligence indicators, severity-mapped at Critical=100, High=75, Medium=50, Low=25
• High-severity risk promoted to Azure Security Center and Microsoft Defender for Cloud as native incidents, with no custom engineering
• Native integrations with Microsoft Sentinel, Microsoft Defender, CrowdStrike Falcon, and AWS Security Hub

This means an AI agent attempting prompt injection, data exfiltration, or lateral tool access can surface in the same investigation queue your analysts already work from.

Auditability as a first-class outcome

Every decision Vaikora makes is written to a SHA-256 hash-chained, tamper-evident audit log. The chain gives you an ordered record that cannot be altered without detection. For compliance purposes, this means you can reconstruct exactly what an agent attempted, what policy applied, and what decision was made, with a verifiable integrity guarantee.

That matters for regulated environments where "we think the agent behaved correctly" is not an acceptable answer. The audit log makes the answer answerable.

Deployment options

Vaikora ships in two parts. The gateway, called Vaikora Guard MCP, is open source under the MIT license and published on PyPI as `vaikora-guard-mcp`. You can self-host it. The control plane, where you author and manage policy, is the commercial product hosted by Data443, and is available on AWS Marketplace with support for EDP draw-down, and in the Azure Sentinel content hub as the Vaikora-AzureSecurityCenter solution.

What to check next

If you are running AI agents in any environment that touches regulated data or sensitive infrastructure, the first thing to verify is whether any policy gate exists at the action layer. If your current answer is "the LLM has instructions not to do X," that is a prompt-level guardrail, not an enforcement control. Test it by asking the model directly whether it can be overridden. Then look at what happens when a tool call bypasses the model entirely.

Start with the open-source proxy, deploy it against a single agent, and examine what the audit log shows in the first hour of real traffic. That will tell you more than any architecture review.

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